Josh Betz (jbetz@jhu.edu) 2024-02-20
Investigators are faced with many challenges in designing efficient, ethical randomized trials due to many competing demands. A trial must collect enough information to identify meaningful benefits or harms with a desired probability, while also minimizing potential harm and suboptimal treatment of participants. Satisfying these competing demands is further complicated by the limited and imprecise information available during the design of a study.
Rather than planning analyses around sample sizes, investigators can plan analyses which occur when a specified level of precision is reached: this is known as information monitoring (Mehta and Tsiatis 2001). A precision-adaptive design can reduce the risk of under- or overpowered trials by collecting data until the precision is sufficient to conduct analyses. The precision of an estimator is simply the reciprocal of the variance of the estimator. Since this is unknown in practice, the variance is estimated using the square of the standard error of the estimator.
PICARD is an app for helping investigators plan precision-adaptive (i.e. information monitored) study designs. Users can get an idea of what sample size might be required to achieve a desired level of power based on preliminary estimates of population parameters. Planning can be done for continuous, binary, and ordinal outcomes.
PICARD can be used for both fixed sample size design with one analysis and study designs with pre-planned interim analyses for early stopping (Jennison and Turnbull 1999). Plots show estimates of the information expected to accrue for a given sample size, and when interim analyses may be conducted. Actual information levels will vary, and must be monitored during an ongoing study.
Rather than performing analyses based on accrued outcomes, investigators compare the standard error of their estimate, and perform their analysis when sufficient precision is reached. This avoids wasting resources when sufficient information on potential benefits or harms has already been accrued.
Information monitoring can integrate both interim analyses and covariate adjustment using a broad class of methods (Van Lancker, Betz, and Rosenblum 2022). For more information, see the ‘Background’ tab in the app.
PICARD can be run on ShinyApps.io or downloaded for local use.
Jennison, Christopher, and Bruce W. Turnbull. 1999. Group Sequential Methods with Applications to Clinical Trials. Chapman; Hall/CRC. https://doi.org/10.1201/9780367805326.
Mehta, Cyrus R., and Anastasios A. Tsiatis. 2001. “Flexible Sample Size Considerations Using Information-Based Interim Monitoring.” Drug Information Journal 35 (4): 1095–1112. https://doi.org/10.1177/009286150103500407.
Van Lancker, Kelly, Joshua Betz, and Michael Rosenblum. 2022. “Combining Covariate Adjustment with Group Sequential, Information Adaptive Designs to Improve Randomized Trial Efficiency.” arXiv Preprint arXiv:1409.0473. https://doi.org/10.48550/ARXIV.2201.12921.